This report uses a bark scale, which has been replaced here with a mel-scale. CHAPTER 3. THE ABBOT SPEECH RECOGNITION SYSTEM 32 where, ¯ i = 1

In this report system identification techniques are applied to speech. The purpose is to compare different models and different parameter estimation techniques on both a theoretical and an empirical basis. Results are then used in the practical problem of speech enhancement in additive white Gaussian noise. Two model families are identified: polynomial and state space models. These are compared within a common state space framework, which makes explicit the assumptions and constraints of different models regarding process noise, observation noise, input-output delays and initial conditions. State space models are then cast in block matrix form because this representation is used in subspace state space system identification (4SID) methods. Next, three common parameter estimation techniques are compared: prediction error minimisation (PEM), instrumental variables (IV) and 4SID. These models and parameter estimation techniques are compared through experiments on real clean and noisy spee...

Recognition of human speech has long been a hot topic among artificial intelligence and signal processing researchers. In this paper, Pattern recognition and q-Bernstein polynomials have been combined to create computer software designed for the recognition of human speech, and the main principles of speech recognition have been explained in a systematic content. q-Bernstein polynomials, which are mathematical operators, have been applied for pattern recognition, and a new method has thus been developed. Software has been prepared using the Delphi 7 programming language, and with this software, this method has been applied for the processing of verbal expression recognition. In the program as developed, 16 000 samples of 8-bit stereo images were computerized. Speech recognition tests were conducted for six words, and information related to the results of this test is provided in this paper.